From 9dfad1facd48d2dd3b09c6be215021229bfb1057 Mon Sep 17 00:00:00 2001 From: Parikshit Ram Date: Wed, 20 Feb 2008 04:25:31 +0000 Subject: [PATCH] faster folders removed --- fastlib/u/pram/mog_em/faster/build.py | 26 - fastlib/u/pram/mog_em/faster/data.arff | 1000 ----------------- fastlib/u/pram/mog_em/faster/math_functions.h | 138 --- fastlib/u/pram/mog_em/faster/mog.cc | 301 ----- fastlib/u/pram/mog_em/faster/mog.h | 244 ---- fastlib/u/pram/mog_em/faster/mog_em_main.cc | 72 -- fastlib/u/pram/mog_em/faster/phi.h | 142 --- fastlib/u/pram/mog_l2e/faster/build.py | 26 - fastlib/u/pram/mog_l2e/faster/mog.cc | 502 --------- fastlib/u/pram/mog_l2e/faster/mog.h | 595 ---------- .../mog_l2e/faster/mog_l2e_c_expt_data.csv | 1000 ----------------- fastlib/u/pram/mog_l2e/faster/mog_l2e_main.cc | 145 --- fastlib/u/pram/mog_l2e/faster/phi.h | 156 --- 13 files changed, 4347 deletions(-) delete mode 100644 fastlib/u/pram/mog_em/faster/build.py delete mode 100644 fastlib/u/pram/mog_em/faster/data.arff delete mode 100644 fastlib/u/pram/mog_em/faster/math_functions.h delete mode 100644 fastlib/u/pram/mog_em/faster/mog.cc delete mode 100644 fastlib/u/pram/mog_em/faster/mog.h delete mode 100644 fastlib/u/pram/mog_em/faster/mog_em_main.cc delete mode 100644 fastlib/u/pram/mog_em/faster/phi.h delete mode 100644 fastlib/u/pram/mog_l2e/faster/build.py delete mode 100644 fastlib/u/pram/mog_l2e/faster/mog.cc delete mode 100644 fastlib/u/pram/mog_l2e/faster/mog.h delete mode 100644 fastlib/u/pram/mog_l2e/faster/mog_l2e_c_expt_data.csv delete mode 100644 fastlib/u/pram/mog_l2e/faster/mog_l2e_main.cc delete mode 100644 fastlib/u/pram/mog_l2e/faster/phi.h diff --git a/fastlib/u/pram/mog_em/faster/build.py b/fastlib/u/pram/mog_em/faster/build.py deleted file mode 100644 index 6ec98e8330..0000000000 --- a/fastlib/u/pram/mog_em/faster/build.py +++ /dev/null @@ -1,26 +0,0 @@ - -librule( - name = "mog_em", # this line can be safely omitted - sources = ["mog.cc"], # files that must be compiled - headers = ["mog.h","phi.h","math_functions.h"],# include files part of the 'lib' - deplibs = ["fastlib:fastlib"], # depends on fastlib core - #tests = ["mog_em_tests.cc"] # this file contains a main with test functions - ) - -binrule( - name = "mog_em_main", # the executable name - sources = ["mog_em_main.cc"], # compile main.cc - #headers = ["mog_em_main.h"], # no extra headers - deplibs = [":mog_em", "fastlib:fastlib"] # - ) - -# to build: -# 1. make sure have environment variables set up: -# $ source /full/path/to/fastlib/script/fl-env /full/path/to/fastlib -# (you might want to put this in bashrc) -# 2. fl-build main -# - this automatically will assume --mode=check, the default -# - type fl-build --help for help -# 3. ./main -# - to build same target again, type: make -# - to force recompilation, type: make clean diff --git a/fastlib/u/pram/mog_em/faster/data.arff b/fastlib/u/pram/mog_em/faster/data.arff deleted file mode 100644 index d95f367d0f..0000000000 --- a/fastlib/u/pram/mog_em/faster/data.arff +++ /dev/null @@ -1,1000 +0,0 @@ --0.28992,-0.13173,-0.61575 --1.2142,-1.1994,-0.4661 -0.031128,0.084721,-0.057328 --0.86099,-0.79692,-0.35194 --0.45353,-0.11696,-0.15629 -0.82319,0.31796,0.5654 -0.87603,0.62511,0.54203 --1.8458,-1.2202,-1.6845 -1.3571,0.86032,0.95889 --0.88157,-0.092799,-0.21391 --1.7254,-0.80534,-1.2231 --3.7444,-2.7681,-2.8587 --1.9593,-1.9499,-1.6215 -0.036513,-0.65684,-0.16414 --1.0091,-0.9538,-0.82084 --2.6299,-2.4503,-2.0805 -1.4404,0.91235,1.0368 -2.3232,1.7905,1.3835 --0.22953,0.29435,-0.55121 -0.52442,0.53273,0.29293 -1.9345,1.1211,1.5277 --0.71909,-0.91217,-0.13798 --1.0023,-0.31518,-0.83793 -0.88445,0.55393,0.63455 --0.14286,0.75819,0.10212 --0.57519,-0.41389,-0.16138 --0.54536,-0.54785,0.19468 -0.18259,-0.038652,-0.11602 -1.5583,1.5063,1.1687 -0.67228,0.25056,0.28417 -1.8047,1.3339,1.8819 -1.6772,1.8611,1.5816 -0.30442,0.39109,0.31353 --0.66552,-0.38255,-0.36366 --0.99614,-0.31771,0.10233 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--0.24206,1.3421,-1.4736 --0.25654,2.2437,-0.99187 -0.014638,1.7969,-1.4376 --0.1745,1.9168,-1.5279 -0.11876,1.9746,-0.49863 -1.6836,3.1278,-0.030485 --1.7754,0.26854,-2.9489 --1.1159,0.95009,-1.4111 --0.8181,1.4136,-1.5501 --0.54368,1.8649,-1.1626 --1.1901,1.5496,-1.6707 --2.0537,0.3338,-2.5033 --0.24753,1.8794,-1.0107 --0.72903,1.8224,-1.6435 -0.038166,1.9963,-0.67844 --0.638,1.774,-1.5228 -0.42271,1.9838,-1.1418 --1.2348,0.98551,-1.8549 --1.7239,1.1747,-2.1135 --0.015513,1.3959,-0.86992 -1.7031,3.0449,-0.25698 --1.3881,1.0682,-2.1338 --1.1969,1.7697,3.9208 --1.2424,1.4604,3.6047 --1.3163,0.93889,2.8365 --2.8288,0.045368,2.2851 --2.7684,0.13886,1.9198 --0.60191,2.4719,3.8042 --0.39537,1.8476,4.2524 -0.44816,2.6351,4.8098 --1.0442,1.5827,3.8989 --3.6402,0.14778,1.9706 --2.45,0.78979,2.5683 --1.7225,0.73554,3.158 --0.69631,2.2939,4.1272 --2.8072,-0.069145,1.7978 --1.6416,1.5689,3.3282 --3.0648,0.17403,2.4512 --2.0258,0.87394,3.389 --2.5863,0.45752,2.9173 --1.5265,1.8141,4.0588 --4.2433,-0.17808,1.6443 --2.9551,0.18985,1.9159 --1.2529,1.4453,3.5425 --1.8285,0.64726,2.5959 --3.1628,0.046687,1.7174 --1.6728,1.4828,3.2985 --1.6953,1.3002,3.2336 --1.6847,0.97183,3.1077 --1.5924,1.4341,3.4936 --0.70679,1.4356,3.8973 --2.3524,0.85287,2.8275 --2.3738,0.65846,2.3619 --3.539,-0.56682,2.2519 --1.0962,1.2734,3.7768 --3.8573,-0.16291,1.432 --3.8752,-0.40234,2.4318 -0.33871,2.711,5.3221 --2.0428,1.2251,3.07 --1.9655,1.2908,2.9416 --3.0731,0.43096,2.0358 --1.9089,1.0828,2.672 --1.3803,1.7463,3.5046 --0.034521,1.9667,4.0039 --1.1235,1.1656,3.2068 --1.8109,1.4349,3.0943 --2.0088,1.0847,2.9151 --0.31351,1.8273,3.5788 --1.295,1.4664,3.2349 --1.2371,1.9161,4.2493 --2.7012,0.30699,2.9857 --3.0858,0.35541,2.2666 --2.5561,0.22266,1.7668 --2.0615,0.95838,2.3282 --6.4281,-2.209,0.059564 --0.61075,1.7083,3.6271 -0.72189,2.6927,4.7195 --2.3989,0.63415,3.1581 --1.0935,1.3178,3.712 --2.2726,0.72421,2.978 --0.70414,1.7072,3.6075 --0.55165,1.7327,4.2121 --1.3224,0.73273,3.2907 --1.4177,1.6338,3.6401 -0.068642,1.954,4.1146 --3.7918,-0.48566,1.8536 --2.5556,0.73785,2.9208 --1.5831,1.1366,3.1914 -0.41068,2.7159,4.8655 --1.428,1.1453,3.0639 --0.46294,1.8412,4.3182 -0.22348,2.0181,4.379 --2.2658,1.0376,2.7409 --3.591,0.30847,1.7559 --2.0455,0.58627,2.9203 -0.93646,3.1056,5.0571 --0.70905,1.8272,3.9991 --1.1255,1.9211,3.5726 -0.37208,3.0153,4.8578 -0.27377,2.3156,4.8376 --0.53999,2.2613,4.4015 --2.1156,0.66801,2.4573 -0.015444,2.664,4.6196 --1.7627,0.95155,3.4429 --2.6128,0.63736,2.3568 --7.4142,-3.0846,-0.83159 --2.9964,-0.16066,2.233 --2.5093,-0.088607,2.1958 --5.7356,-1.9153,0.27291 --1.7637,1.2192,3.3577 --3.0144,0.33408,2.272 --1.1035,2.0463,3.6744 -0.29377,2.7,4.0339 -7.6709,-0.65356,-6.146 --1.2698,0.37012,3.5987 --3.8242,-5.9383,8.3664 -6.4851,1.6129,-9.0761 -3.7499,1.954,7.6171 -0.60828,3.1314,-1.5524 --6.4482,1.6296,-0.8863 -7.9403,5.2769,2.3478 -6.1994,-6.7288,4.8542 --2.2174,6.795,9.9198 --0.42004,9.9264,8.5588 -2.148,3.2027,-9.6178 -3.0381,-7.5349,-2.3754 -5.2505,1.7427,3.1399 --1.0458,-7.0753,-9.4356 -2.8597,8.9945,2.2305 --4.3883,4.8396,0.47878 --1.1343,7.4522,2.4586 --5.0359,-6.7081,-7.2111 --6.9676,-0.5243,-0.97505 --5.8695,-3.0673,-1.8167 -8.2832,-4.0377,8.1787 -1.6522,-9.1964,-1.9773 -3.8661,-2.368,-6.1853 -7.1298,-7.3613,6.3322 --0.27049,-7.1403,-1.549 --3.1454,-2.3609,-2.2842 --1.6271,2.7362,4.3218 --7.6258,5.6069,2.1053 -0.37131,-1.4426,-8.3637 -9.4317,-6.5751,-7.3919 --3.4185,-2.6663,-2.6426 --3.2166,9.5563,1.2013 -6.0375,-0.81393,4.8038 --6.806,7.4827,9.649 --6.8151,-6.9582,-0.28445 --6.7267,-1.1509,-9.9922 -8.287,8.4159,5.6332 -5.5004,9.6078,-3.0566 --1.4023,-0.72913,-4.0823 --4.8169,-3.1102,6.6306 -2.4477,-5.8111,-8.0409 -9.705,-3.6756,-2.5232 -2.7386,7.9568,2.8053 --5.3387,-8.5872,-9.3185 -5.69,-5.0415,-3.6123 --9.9319,-9.6028,9.2331 -5.9482,9.9372,3.3279 --5.8469,-7.9974,-8.2701 --6.0094,8.3772,-4.8887 -9.5516,-7.3199,-7.1452 -2.4614,-5.899,-4.4542 --8.8957,-6.7169,5.5289 --7.154,5.541,4.1596 --1.8432,1.2028,2.0585 -5.563,3.3022,-9.2116 --6.7447,7.4713,6.9136 --9.4759,2.1966,-5.4052 -7.0271,8.7768,-0.098737 --4.9415,-4.2886,-9.3987 --2.0969,5.2979,2.9589 -3.7458,-7.0135,-1.9911 -7.4198,2.3875,5.3503 -3.6197,2.4641,-2.3227 --7.216,6.167,7.8282 -1.5398,-3.6429,-1.8442 -3.6883,-3.0284,-2.4634 --3.6761,-4.9286,-8.231 -0.2367,8.4299,9.6515 --1.078,4.0697,8.4339 -0.40362,4.1245,-0.76191 --2.6794,-6.9877,1.2293 -3.0462,4.0747,-0.13222 -7.4312,-8.5241,9.8795 -7.3594,-8.9495,-9.4865 -0.59609,0.47255,-4.8097 -9.1863,2.9909,-3.4567 -1.8203,-5.3323,-7.5425 --9.5337,-7.1973,4.9186 --6.3554,-5.2483,2.5872 --0.65065,7.4834,-6.0681 -2.2684,7.2147,-0.72463 --7.2069,3.0322,-4.6809 --5.7646,7.4828,-9.9276 -9.8014,9.2737,-7.6849 --3.3992,8.9312,1.1783 --1.4833,7.7265,7.8454 --7.0749,0.75356,8.0241 -0.91467,6.2634,3.9977 --8.1604,-5.15,-5.3245 --1.3009,-2.721,0.59003 --8.3264,3.5507,6.0953 -1.0982,-6.0583,3.9963 --9.6435,-6.1097,-9.9918 -2.8821,1.0076,-4.2984 --2.1547,-9.6324,-6.5842 --3.4672,-6.7797,-5.9353 --4.7036,0.70339,-8.1064 -0.20557,-1.4378,-9.2265 --0.88981,-1.8582,-2.1328 -6.2075,6.6453,-5.3313 diff --git a/fastlib/u/pram/mog_em/faster/math_functions.h b/fastlib/u/pram/mog_em/faster/math_functions.h deleted file mode 100644 index 2349bc9d06..0000000000 --- a/fastlib/u/pram/mog_em/faster/math_functions.h +++ /dev/null @@ -1,138 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file math_functions.h - * - * This file has certain functions that find the - * highest or lowest element in an array or - * in a row of a matrix and returns them - * - */ - -#include "fastlib/fastlib.h" -#include "fastlib/fastlib_int.h" - - -/** - * Finds the index of the minimum element - * in each row of a matrix - * - * Example use: - * @code - * Matrix& mat; - * index_t indices[mat.n_rows()]; - * - * ... - * - * min_element(mat, indices); - * @endcode - */ - -void min_element(Matrix& element, index_t *indices) { - - index_t last = element.n_cols() - 1; - index_t first, lowest; - index_t i; - - for (i = 0; i < element.n_rows(); i++) { - - first = lowest = 0; - if (first == last) { - indices[i] = last; - } - while (++first <= last) { - if (element.get(i, first) < element.get(i, lowest)) { - lowest = first; - } - } - indices[i] = lowest; - } - return; -} - -/** - * Returns the index of the maximum element - * in a float array 'array' of length 'length'. - * - * Example use: - * @code - * index_t length, index; - * float array[length]; - * ... - * index = max_element_index(array, length); - * @endcode - */ -int max_element_index(float *array, int length) { - - int last = length - 1; - int first = 0; - int highest = 0; - - if (first == last) { - return last; - } - while (++first <= last) { - if (array[first] > array[highest]) { - highest = first; - } - } - return highest; -} - -/** - * Finds the index of the maximum element in an arraylist - * of floats - * - * Example use: - * @code - * index_t index; - * ArrayList array; - * ... - * index = max_element_index(array); - * @endcode - */ -int max_element_index(ArrayList& array){ - - int last = array.size() - 1; - int first = 0; - int highest = 0; - - if (first == last) { - return last; - } - while (++first <= last) { - if (array[first] > array[highest]) { - highest = first; - } - } - return highest; -} - -/** - * Returns the index of the maximum element in - * an arraylist of doubles - * - * Example use: - * @code - * index_t index; - * ArrayList array; - * ... - * index = max_element_index(array); - * @endcode - */ -int max_element_index(ArrayList& array) { - - int last = array.size() - 1; - int first = 0; - int highest = 0; - - if (first == last) { - return last; - } - while (++first <= last) { - if(array[first] > array[highest]) { - highest = first; - } - } - return highest; -} - diff --git a/fastlib/u/pram/mog_em/faster/mog.cc b/fastlib/u/pram/mog_em/faster/mog.cc deleted file mode 100644 index 85d377d2ce..0000000000 --- a/fastlib/u/pram/mog_em/faster/mog.cc +++ /dev/null @@ -1,301 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file mog.cc - * - * Implementation for the loglikelihood function, the EM algorithm - * and also computes the K-means for getting an initial point - * - */ - -#include "mog.h" -#include "phi.h" -#include "math_functions.h" - -void MoGEM::ExpectationMaximization(Matrix& data_points) { - - // Declaration of the variables */ - index_t num_points; - index_t dim, num_gauss; - double sum, tmp; - ArrayList mu_temp, mu; - ArrayList sigma_temp, sigma; - Vector omega_temp, omega, x; - Matrix cond_prob; - long double l, l_old, best_l, INFTY = 99999, TINY = 1.0e-10; - - // Initializing values - dim = dimension(); - num_gauss = number_of_gaussians(); - num_points = data_points.n_cols(); - - // Initializing the number of the vectors and matrices - // according to the parameters input - mu_temp.Init(num_gauss); - mu.Init(num_gauss); - sigma_temp.Init(num_gauss); - sigma.Init(num_gauss); - omega_temp.Init(num_gauss); - omega.Init(num_gauss); - - // Allocating size to the vectors and matrices - // according to the dimensionality of the data - for(index_t i = 0; i < num_gauss; i++) { - mu_temp[i].Init(dim); - mu[i].Init(dim); - sigma_temp[i].Init(dim, dim); - sigma[i].Init(dim, dim); - } - x.Init(dim); - cond_prob.Init(num_gauss, num_points); - - best_l = -INFTY; - index_t restarts = 0; - // performing 5 restarts and choosing the best from them - while (restarts < 1) { - - // assign initial values to 'mu', 'sig' and 'omega' using k-means - KMeans(data_points, &mu_temp, &sigma_temp, &omega_temp, num_gauss); - - l_old = -INFTY; - - // calculates the loglikelihood value - l = Loglikelihood(data_points, mu_temp, sigma_temp, omega_temp); - - // added a check here to see if any - // significant change is being made - // at every iteration - while (l - l_old > TINY) { - // calculating the conditional probabilities - // of choosing a particular gaussian given - // the data and the present theta value - /********** - for (index_t j = 0; j < num_points; j++) { - x.CopyValues(data_points.GetColumnPtr(j)); - sum = 0; - for (index_t i = 0; i < num_gauss; i++) { - tmp = phi(x, mu_temp[i], sigma_temp[i]) * omega_temp.get(i); // can be made faster by sending all the data at once instead of looping over - cond_prob.set(i, j, tmp); - sum += tmp; - } - for (index_t i = 0; i < num_gauss; i++) { - tmp = cond_prob.get(i, j); - cond_prob.set(i, j, tmp / sum); - } - } - ******/ - - /*******FIX THIS SOON********/ - Vector phi_val; - phi_val.Init(num_points); - - for(index_t i = 0; i < num_gauss; i++) { - phi_val.CopyValues(phi(data_points, mu_temp[i], sigma_temp[i])); - la::Scale(omega_temp.get(i), &phi_val); - */ - - // calculating the new value of the mu - // using the updated conditional probabilities - for (index_t i = 0; i < num_gauss; i++) { - sum = 0; - mu_temp[i].SetZero(); - for (index_t j = 0; j < num_points; j++) { - x.CopyValues(data_points.GetColumnPtr(j)); - la::AddExpert(cond_prob.get(i, j), x, &mu_temp[i]); - sum += cond_prob.get(i, j); - } - la::Scale((1.0 / sum), &mu_temp[i]); - } - - // calculating the new value of the sig - // using the updated conditional probabilities - // and the updated mu - for (index_t i = 0; i < num_gauss; i++) { - sum = 0; - sigma_temp[i].SetZero(); - for (index_t j = 0; j < num_points; j++) { - Matrix co, ro, c; - c.Init(dim, dim); - x.CopyValues(data_points.GetColumnPtr(j)); - la::SubFrom(mu_temp[i] , &x); - co.AliasColVector(x); - ro.AliasRowVector(x); - la::MulOverwrite(co, ro, &c); - la::AddExpert(cond_prob.get(i, j), c, &sigma_temp[i]); - sum += cond_prob.get(i, j); - } - la::Scale((1.0 / sum), &sigma_temp[i]); - } - - // calculating the new values for omega - // using the updated conditional probabilities - Vector identity_vector; - identity_vector.Init(num_points); - identity_vector.SetAll(1.0 / num_points); - la::MulOverwrite(cond_prob, identity_vector, &omega_temp); - - l_old = l; - l = Loglikelihood(data_points, mu_temp, sigma_temp, omega_temp); - } - - // putting a check to see if the best one is chosen - if(l > best_l){ - best_l = l; - for (index_t i = 0; i < num_gauss; i++) { - mu[i].CopyValues(mu_temp[i]); - sigma[i].CopyValues(sigma_temp[i]); - } - omega.CopyValues(omega_temp); - } - restarts++; - } - - for (index_t i = 0; i < num_gauss; i++) { - set_mu(i, mu[i]); - set_sigma(i, sigma[i]); - } - set_omega(omega); - - NOTIFY("loglikelihood value of the estimated model: %Lf\n", best_l); - return; -} - -long double MoGEM::Loglikelihood(Matrix& data_points, ArrayList& means, - ArrayList& covars, Vector& weights) { - - index_t i, j; - Vector x; - long double likelihood, loglikelihood = 0; - - x.Init(data_points.n_rows()); - - for (j = 0; j < data_points.n_cols(); j++) { - x.CopyValues(data_points.GetColumnPtr(j)); - likelihood = 0; - for(i = 0; i < number_of_gaussians() ; i++){ - likelihood += weights.get(i) * phi(x, means[i], covars[i]); - } - loglikelihood += log(likelihood); - } - return loglikelihood; -} - -void MoGEM::KMeans(Matrix& data, ArrayList *means, - ArrayList *covars, Vector *weights, index_t value_of_k){ - - - ArrayList mu, mu_old; - double* tmpssq; - double* sig; - double* sig_best; - index_t *y; - Vector x, diff; - Matrix ssq; - index_t i, j, k, n, t, dim; - double score, score_old, sum; - - n = data.n_cols(); - dim = data.n_rows(); - mu.Init(value_of_k); - mu_old.Init(value_of_k); - tmpssq = (double*)malloc(value_of_k * sizeof( double )); - sig = (double*)malloc(value_of_k * sizeof( double )); - sig_best = (double*)malloc(value_of_k * sizeof( double )); - ssq.Init(n, value_of_k); - - for( i = 0; i < value_of_k; i++){ - mu[i].Init(dim); - mu_old[i].Init(dim); - } - x.Init(dim); - y = (index_t*)malloc(n * sizeof(index_t)); - diff.Init(dim); - - score_old = 999999; - - // putting 5 random restarts to obtain the k-means - for(i = 0; i < 5; i++){ - t = -1; - for (k = 0; k < value_of_k; k++){ - t = (t + 1 + (rand()%((n - 1 - (value_of_k - k)) - (t + 1)))); - mu[k].CopyValues(data.GetColumnPtr(t)); - for(j = 0; j < n; j++){ - x.CopyValues( data.GetColumnPtr(j)); - la::SubOverwrite(mu[k], x, &diff); - ssq.set( j, k, la::Dot(diff, diff)); - } - } - min_element(ssq, y); - - do{ - for(k = 0; k < value_of_k; k++){ - mu_old[k].CopyValues(mu[k]); - } - - for(k = 0; k < value_of_k; k++){ - index_t p = 0; - mu[k].SetZero(); - for(j = 0; j < n; j++){ - x.CopyValues(data.GetColumnPtr(j)); - if(y[j] == k){ - la::AddTo(x, &mu[k]); - p++; - } - } - - if(p == 0){ - } - else{ - double sc = 1 ; - sc = sc / p; - la::Scale(sc , &mu[k]); - } - for(j = 0; j < n; j++){ - x.CopyValues(data.GetColumnPtr(j)); - la::SubOverwrite(mu[k], x, &diff); - ssq.set(j, k, la::Dot(diff, diff)); - } - } - min_element(ssq, y); - - sum = 0; - for(k = 0; k < value_of_k; k++) { - la::SubOverwrite(mu[k], mu_old[k], &diff); - sum += la::Dot(diff, diff); - } - }while(sum != 0); - - for(k = 0; k < value_of_k; k++){ - index_t p = 0; - tmpssq[k] = 0; - for(j = 0; j < n; j++){ - if(y[j] == k){ - tmpssq[k] += ssq.get(j, k); - p++; - } - } - sig[k] = sqrt(tmpssq[k] / p); - } - - score = 0; - for(k = 0; k < value_of_k; k++){ - score += tmpssq[k]; - } - score = score / n; - - if (score < score_old) { - score_old = score; - for(k = 0; k < value_of_k; k++){ - (*means)[k].CopyValues(mu[k]); - sig_best[k] = sig[k]; - } - } - } - - for(k = 0; k < value_of_k; k++){ - x.SetAll(sig_best[k]); - (*covars)[k].SetDiagonal(x); - } - double tmp = 1; - (*weights).SetAll(tmp / value_of_k); - return; -} diff --git a/fastlib/u/pram/mog_em/faster/mog.h b/fastlib/u/pram/mog_em/faster/mog.h deleted file mode 100644 index 3acefe9bca..0000000000 --- a/fastlib/u/pram/mog_em/faster/mog.h +++ /dev/null @@ -1,244 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file mog.h - * - * Defines a Gaussian Mixture model and - * estimates the parameters of the model - */ - -#ifndef MOGEM_H -#define MOGEM_H - -#include - -/** - * A Gaussian mixture model class. - * - * This class uses maximum likelihood loss functions to - * estimate the parameters of a gaussian mixture - * model on a given data via the EM algorithm. - * - * - * Example use: - * - * @code - * MoGEM mog; - * ArrayList results; - * - * mog.Init(number_of_gaussians, dimension); - * mog.ExpectationMaximization(data, &results, optim_flag); - * @endcode - */ -class MoGEM { - - private: - - // The parameters of the mixture model - ArrayList mu_; - ArrayList sigma_; - Vector omega_; - index_t number_of_gaussians_; - index_t dimension_; - - public: - - MoGEM() { - mu_.Init(0); - sigma_.Init(0); - } - - ~MoGEM() { - } - - void Init(index_t num_gauss, index_t dimension) { - - // Initialize the private variables - number_of_gaussians_ = num_gauss; - dimension_ = dimension; - - // Resize the ArrayList of Vectors and Matrices - mu_.Resize(number_of_gaussians_); - sigma_.Resize(number_of_gaussians_); - } - - void Init(datanode *mog_em_module) { - - index_t num_gauss = fx_param_int_req(mog_em_module, "K"); - index_t dim = fx_param_int_req(mog_em_module, "D"); - Init(num_gauss, dim); - } - // The get functions - - ArrayList& mu() { - return mu_; - } - - ArrayList& sigma() { - return sigma_; - } - - Vector& omega() { - return omega_; - } - - index_t number_of_gaussians() { - return number_of_gaussians_; - } - - index_t dimension() { - return dimension_; - } - - Vector& mu(index_t i) { - return mu_[i] ; - } - - Matrix& sigma(index_t i) { - return sigma_[i]; - } - - double omega(index_t i) { - return omega_.get(i); - } - - // The set functions - - void set_mu(index_t i, Vector& mu) { - DEBUG_ASSERT(i < number_of_gaussians()); - DEBUG_ASSERT(mu.length() == dimension()); - mu_[i].Copy(mu); - return; - } - - void set_mu(index_t i, index_t length, const double *mu) { - DEBUG_ASSERT(i < number_of_gaussians()); - DEBUG_ASSERT(length == dimension()); - mu_[i].Copy(mu, length); - return; - } - - void set_sigma(index_t i, Matrix& sigma) { - DEBUG_ASSERT(i < number_of_gaussians()); - DEBUG_ASSERT(sigma.n_rows() == dimension()); - DEBUG_ASSERT(sigma.n_cols() == dimension()); - sigma_[i].Copy(sigma); - return; - } - - void set_omega(Vector& omega) { - DEBUG_ASSERT(omega.length() == number_of_gaussians()); - omega_.Copy(omega); - return; - } - - void set_omega(index_t length, const double *omega) { - DEBUG_ASSERT(length == number_of_gaussians()); - omega_.Copy(omega, length); - return; - } - - - /** - * This function outputs the parameters of the model - * to an arraylist of doubles - * - * @code - * ArrayList results; - * mog.OutputResults(&results); - * @endcode - */ - void OutputResults(ArrayList *results) { - - // Initialize the size of the output array - (*results).Init(number_of_gaussians_ * (1 + dimension_*(1 + dimension_))); - - // Copy values to the array from the private variables of the class - for (index_t i = 0; i < number_of_gaussians_; i++) { - (*results)[i] = omega(i); - for (index_t j = 0; j < dimension_; j++) { - (*results)[number_of_gaussians_ + i*dimension_ + j] = mu(i).get(j); - for (index_t k = 0; k < dimension_; k++) { - (*results)[number_of_gaussians_*(1 + dimension_) - + i*dimension_*dimension_ + j*dimension_ - + k] = sigma(i).get(j, k); - } - } - } - } - - /** - * This function prints the parameters of the model - * - * @code - * mog.Display(); - * @endcode - */ - void Display(){ - - // Output the model parameters as the omega, mu and sigma - printf(" Omega : [ "); - for (index_t i = 0; i < number_of_gaussians_; i++) { - printf("%lf ", omega(i)); - } - printf("]\n"); - printf(" Mu : \n["); - for (index_t i = 0; i < number_of_gaussians_; i++) { - for (index_t j = 0; j < dimension_ ; j++) { - printf("%lf ", mu(i).get(j)); - } - printf(";"); - if (i == (number_of_gaussians_ - 1)) { - printf("\b]\n"); - } - } - printf("Sigma : "); - for (index_t i = 0; i < number_of_gaussians_; i++) { - printf("\n["); - for (index_t j = 0; j < dimension_ ; j++) { - for(index_t k = 0; k < dimension_ ; k++) { - printf("%lf ",sigma(i).get(j, k)); - } - printf(";"); - } - printf("\b]"); - } - printf("\n"); - } - - /** - * This function calculates the parameters of the model - * using the Maximum Likelihood function via the - * Expectation Maximization (EM) Algorithm. - * - * @code - * MoG mog; - * Matrix data = "the data on which you want to fit the model"; - * ArrayList results; - * mog.ExpectationMaximization(data, &results); - * @endcode - */ - void ExpectationMaximization(Matrix& data_points); - - /** - * This function computes the loglikelihood of model. - * This function is used by the 'ExpectationMaximization' - * function. - * - */ - long double Loglikelihood(Matrix& data_points, ArrayList& means, - ArrayList& covars, Vector& weights); - - /** - * This function computes the k-means of the data and stores - * the calculated means and covariances in the ArrayList - * of Vectors and Matrices passed to it. It sets the weights - * uniformly. - * - * This function is used to obtain a starting point for - * the optimization - */ - void KMeans(Matrix& data, ArrayList *means, - ArrayList *covars, Vector *weights, index_t value_of_k); -}; - -#endif diff --git a/fastlib/u/pram/mog_em/faster/mog_em_main.cc b/fastlib/u/pram/mog_em/faster/mog_em_main.cc deleted file mode 100644 index 6e64a9bf87..0000000000 --- a/fastlib/u/pram/mog_em/faster/mog_em_main.cc +++ /dev/null @@ -1,72 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file mog_l2e_main.cc - * - * This program test drives the L2 estimation - * of a Gaussian Mixture model. - * - * PARAMETERS TO BE INPUT: - * - * --data - * This is the file that contains the data on which - * the model is to be fit - * - * --mog_em/K - * This is the number of gaussians we want to fit - * on the data, defaults to '1' - * - * --output - * This file will contain the parameters estimated, - * defaults to 'ouotput.csv' - * - */ - -#include "mog.h" - - -int main(int argc, char* argv[]) { - - fx_init(argc, argv); - - ////// READING PARAMETERS AND LOADING DATA ////// - - const char *data_filename = fx_param_str_req(NULL, "data"); - - Matrix data_points; - data::Load(data_filename, &data_points); - - ////// MIXTURE OF GAUSSIANS USING EM ////// - - MoGEM mog; - - struct datanode* mog_em_module = fx_submodule(NULL, "mog_em", "mog_em"); - fx_param_int(mog_em_module, "K", 1); - fx_format_param(mog_em_module, "D", "%d", data_points.n_rows()); - - ////// Timing the initialization of the mixture model ////// - fx_timer_start(mog_em_module, "model_init"); - mog.Init(mog_em_module); - fx_timer_stop(mog_em_module, "model_init"); - - ////// Computing the parameters of the model using the EM algorithm ////// - ArrayList results; - - fx_timer_start(mog_em_module, "EM"); - mog.ExpectationMaximization(data_points); - fx_timer_stop(mog_em_module, "EM"); - - mog.Display(); - mog.OutputResults(&results); - - ////// OUTPUT RESULTS ////// - - const char *output_filename = fx_param_str(NULL, "output", "output.csv"); - - FILE *output_file = fopen(output_filename, "w"); - - ot::Print(results, output_file); - fclose(output_file); - fx_done(); - - return 1; -} diff --git a/fastlib/u/pram/mog_em/faster/phi.h b/fastlib/u/pram/mog_em/faster/phi.h deleted file mode 100644 index e7f9a7c2ae..0000000000 --- a/fastlib/u/pram/mog_em/faster/phi.h +++ /dev/null @@ -1,142 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file phi.h - * - * This file computes the Gaussian probability - * density function - */ -#include "fastlib/fastlib.h" -#include "fastlib/fastlib_int.h" -#include - -/** - * Calculates the multivariate Gaussian probability density function - * - * Example use: - * @code - * Vector x, mean; - * Matrix cov; - * .... - * long double f = phi(x, mean, cov); - * @endcode - */ - -long double phi(Vector& x , Vector& mean , Matrix& cov) { - - long double det, f; - double exponent; - index_t dim; - Matrix inv; - Vector diff, tmp; - - dim = x.length(); - la::InverseInit(cov, &inv); - det = la::Determinant(cov); - - if( det < 0){ - det = -det; - } - la::SubInit(mean,x,&diff); - la::MulInit(inv, diff, &tmp); - exponent = la::Dot(diff, tmp); - long double tmp1, tmp2, tmp3; - tmp1 = 1; - tmp2 = dim; - tmp2 = tmp2/2; - tmp2 = pow((2*(math::PI)),tmp2); - tmp1 = tmp1/tmp2; - tmp3 = 1; - tmp2 = sqrt(det); - tmp3 = tmp3/tmp2; - tmp2 = -exponent; - tmp2 = tmp2 / 2; - f = (tmp1*tmp3*exp(tmp2)); - - return f; -} - -/** - * Calculates the univariate Gaussian probability density function - * - * Example use: - * @code - * double x, mean, var; - * .... - * long double f = phi(x, mean, var); - * @endcode - */ - -long double phi(double x, double mean, double var) { - - long double f; - - f = exp(-1.0*((x-mean)*(x-mean)/(2*var)))/sqrt(2*math::PI*var); - return f; -} - -/** - * Calculates the multivariate Gaussian probability density function - * and also the gradients with respect to the mean and the variance - * - * Example use: - * @code - * Vector x, mean, g_mean, g_cov; - * ArrayList d_cov; // the dSigma - * .... - * long double f = phi(x, mean, cov, d_cov, &g_mean, &g_cov); - * @endcode - */ - -long double phi(Vector& x, Vector& mean, Matrix& cov, ArrayList& d_cov, Vector *g_mean, Vector *g_cov){ - - long double det, f; - double exponent; - index_t dim; - Matrix inv; - Vector diff, tmp; - - dim = x.length(); - la::InverseInit(cov, &inv); - det = la::Determinant(cov); - - if( det < 0){ - det = -det; - } - la::SubInit(mean,x,&diff); - la::MulInit(inv, diff, &tmp); - exponent = la::Dot(diff, tmp); - long double tmp1, tmp2, tmp3; - tmp1 = 1; - tmp2 = dim; - tmp2 = tmp2/2; - tmp2 = pow((2*(math::PI)),tmp2); - tmp1 = tmp1/tmp2; - tmp3 = 1; - tmp2 = sqrt(det); - tmp3 = tmp3/tmp2; - tmp2 = -exponent; - tmp2 = tmp2 / 2; - f = (tmp1*tmp3*exp(tmp2)); - - // Calculating the g_mean values which would be a (1 X dim) vector - la::ScaleInit(f,tmp,g_mean); - - // Calculating the g_cov values which would be a (1 X (dim*(dim+1)/2)) vector - double *g_cov_tmp; - g_cov_tmp = (double*)malloc(d_cov.size()*sizeof(double)); - for(index_t i = 0; i < d_cov.size(); i++){ - Vector tmp_d; - Matrix inv_d; - long double tmp_d_cov_d_r; - - la::MulInit(d_cov[i],tmp,&tmp_d); - tmp_d_cov_d_r = la::Dot(tmp_d,tmp); - la::MulInit(inv,d_cov[i],&inv_d); - for(index_t j = 0; j < dim; j++) - tmp_d_cov_d_r += inv_d.get(j,j); - g_cov_tmp[i] = f*tmp_d_cov_d_r/2; - } - g_cov->Copy(g_cov_tmp,d_cov.size()); - - return f; -} diff --git a/fastlib/u/pram/mog_l2e/faster/build.py b/fastlib/u/pram/mog_l2e/faster/build.py deleted file mode 100644 index a0bc847cdf..0000000000 --- a/fastlib/u/pram/mog_l2e/faster/build.py +++ /dev/null @@ -1,26 +0,0 @@ - -librule( - name = "mog_l2e", # this line can be safely omitted - sources = ["mog.cc"], # files that must be compiled - headers = ["mog.h","phi.h"],# include files part of the 'lib' - deplibs = ["fastlib:fastlib"] # depends on fastlib core - #tests = ["mog_l2e_tests.cc"] - ) - -binrule( - name = "mog_l2e_main", # the executable name - sources = ["mog_l2e_main.cc"], # compile main.cc - headers = ["../opt/optimizers.h"], # no extra headers - deplibs = [":mog_l2e","fastlib:fastlib"] # - ) - -# to build: -# 1. make sure have environment variables set up: -# $ source /full/path/to/fastlib/script/fl-env /full/path/to/fastlib -# (you might want to put this in bashrc) -# 2. fl-build main -# - this automatically will assume --mode=check, the default -# - type fl-build --help for help -# 3. ./main -# - to build same target again, type: make -# - to force recompilation, type: make clean diff --git a/fastlib/u/pram/mog_l2e/faster/mog.cc b/fastlib/u/pram/mog_l2e/faster/mog.cc deleted file mode 100644 index b0719e41e0..0000000000 --- a/fastlib/u/pram/mog_l2e/faster/mog.cc +++ /dev/null @@ -1,502 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file mog.cc - * - * Implementation for L2 loss function, and - * also some initial points generator - * - */ - -#include "mog.h" -#include "phi.h" - -long double MoGL2E::L2Error(const Matrix& data, Vector *gradients) { - - long double reg, fit, l2e; - index_t number_of_points = data.n_cols(); - - - if (gradients != NULL) { - Vector g_reg, g_fit; - reg = RegularizationTerm_(&g_reg); - fit = GoodnessOfFitTerm_(data, &g_fit); - DEBUG_ASSERT(gradients->length() == (number_of_gaussians()* - (dimension()+1)*(dimension()+2)/2 - 1) - - ); - gradients->SetAll(0.0); - la::AddTo(g_reg, gradients); - la::AddExpert((-2.0/number_of_points), g_fit, gradients); - } - else { - reg = RegularizationTerm_(); - fit = GoodnessOfFitTerm_(data); - } - l2e = reg - 2*fit / number_of_points; - return l2e; -} - -long double MoGL2E::RegularizationTerm_(Vector *g_reg){ - Matrix phi_mu, sum_covar; - Vector x, y; - long double reg, tmpVal; - index_t num_gauss, dim; - - Vector df_dw, g_omega; - ArrayList g_mu, g_sigma; - ArrayList > dp_d_mu, dp_d_sigma; - - - num_gauss = number_of_gaussians(); - dim = dimension(); - - phi_mu.Init(num_gauss, num_gauss); - sum_covar.Init(dim, dim); - x.Copy(omega()); - - if (g_reg != NULL) { - g_mu.Init(num_gauss); - g_sigma.Init(num_gauss); - dp_d_mu.Init(num_gauss); - dp_d_sigma.Init(num_gauss); - for(index_t k = 0; k < num_gauss; k++){ - dp_d_mu[k].Init(num_gauss); - dp_d_sigma[k].Init(num_gauss); - } - } - else { - g_mu.Init(0); - g_sigma.Init(0); - dp_d_mu.Init(0); - dp_d_sigma.Init(0); - df_dw.Init(0); - g_omega.Init(0); - } - - for(index_t k = 1; k < num_gauss; k++) { - for(index_t j = 0; j < k; j++) { - la::AddOverwrite(sigma(k), sigma(j), &sum_covar); - - if (g_reg != NULL) { - ArrayList tmp_d_cov; - Vector tmp_dp_d_sigma; - - tmp_d_cov.Init(dim*(dim+1)); - for(index_t i = 0; i < (dim*(dim + 1) / 2); i++){ - tmp_d_cov[i].Copy(d_sigma(k)[i]); - tmp_d_cov[(dim*(dim+1)/2)+i].Copy(d_sigma(j)[i]); - } - - //tmpVal = phi(mu(k),mu(j),sum_covar, - // tmp_d_cov,&dp_d_mu[j][k],&tmp_dp_d_sigma); - tmpVal = phi(mu(k),mu(j),sum_covar,tmp_d_cov, &dp_d_mu[k][j], - &tmp_dp_d_sigma); - - phi_mu.set(j, k, tmpVal); - phi_mu.set(k, j, tmpVal); - - // la::ScaleInit(-1.0, dp_d_mu[j][k], &dp_d_mu[k][j]); - la::ScaleInit(-1.0, dp_d_mu[k][j], &dp_d_mu[j][k]); - - double *tmp_dp, *tmp_dp_1, *tmp_dp_2; - tmp_dp = tmp_dp_d_sigma.ptr(); - tmp_dp_1 = (double*)malloc((tmp_dp_d_sigma.length()/2) * sizeof(double)); - tmp_dp_2 = (double*)malloc((tmp_dp_d_sigma.length()/2) * sizeof(double)); - for(index_t i = 0; i < (tmp_dp_d_sigma.length()/2); i++){ - tmp_dp_1[i] = tmp_dp[i]; - tmp_dp_2[i] = tmp_dp[(dim*(dim + 1) / 2) + i]; - } - dp_d_sigma[j][k].Copy(tmp_dp_1, (dim*(dim + 1) / 2)); - dp_d_sigma[k][j].Copy(tmp_dp_2, (dim*(dim + 1) / 2)); - } - else { - tmpVal = phi(mu(k), mu(j), sum_covar); - phi_mu.set(j, k, tmpVal); - phi_mu.set(k, j, tmpVal); - } - } - } - - for(index_t k = 0; k < num_gauss; k++) { - la::ScaleOverwrite(2, sigma(k), &sum_covar); - - if (g_reg != NULL) { - Vector junk; - tmpVal = phi(mu(k), mu(k), sum_covar, - d_sigma(k), &junk, &dp_d_sigma[k][k]); - phi_mu.set(k, k, tmpVal); - dp_d_mu[k][k].Init(dim); - dp_d_mu[k][k].SetZero(); - } - else { - tmpVal = phi(mu(k), mu(k), sum_covar); - phi_mu.set(k, k, tmpVal); - - } - } - - // Calculating the reg value - la::MulInit( x, phi_mu, &y ); - reg = la::Dot( x, y ); - - if (g_reg != NULL) { - // Calculating the g_omega values - a vector of size K-1 - la::ScaleInit(2.0,y,&df_dw); - la::MulInit(d_omega(),df_dw,&g_omega); - - // Calculating the g_mu values - K vectors of size D - for(index_t k = 0; k < num_gauss; k++){ - g_mu[k].Init(dim); - g_mu[k].SetZero(); - for(index_t j = 0; j < num_gauss; j++) { - la::AddExpert(x.get(j), dp_d_mu[j][k], &g_mu[k]); - } - la::Scale((2.0 * x.get(k)), &g_mu[k]); - } - - // Calculating the g_sigma values - K vectors of size D(D+1)/2 - for(index_t k = 0; k < num_gauss; k++){ - g_sigma[k].Init((dim*(dim + 1)) / 2); - g_sigma[k].SetZero(); - for(index_t j = 0; j < num_gauss; j++) { - la::AddExpert(x.get(j), dp_d_sigma[j][k], &g_sigma[k]); - } - la::Scale((2.0 * x.get(k)), &g_sigma[k]); - } - - // Making the single gradient vector of size K*(D+1)*(D+2)/2 - 1 - double *tmp_g_reg; - tmp_g_reg = (double*)malloc(((num_gauss*(dim + 1)*(dim + 2) / 2) - 1) - *sizeof(double)); - index_t j = 0; - for(index_t k = 0; k < g_omega.length(); k++) { - tmp_g_reg[k] = g_omega.get(k); - } - j = g_omega.length(); - for(index_t k = 0; k < num_gauss; k++){ - for(index_t i = 0; i < dim; i++){ - tmp_g_reg[j + k*(dim) + i] = g_mu[k].get(i); - } - for(index_t i = 0; i < (dim*(dim+1)/2); i++){ - tmp_g_reg[j + num_gauss*dim - + k*(dim*(dim+1) / 2) - + i] = g_sigma[k].get(i); - } - } - g_reg->Copy(tmp_g_reg, ((num_gauss*(dim+1)*(dim+2) / 2) - 1)); - } - - return reg; -} - -long double MoGL2E::GoodnessOfFitTerm_(const Matrix& data, Vector *g_fit) { - long double fit; - Matrix phi_x; - Vector weights, x, y, identity_vector; - index_t num_gauss, num_points, dim; - long double tmpVal; - Vector g_omega,tmp_g_omega; - ArrayList g_mu, g_sigma; - - num_gauss = number_of_gaussians(); - num_points = data.n_cols(); - dim = data.n_rows(); - phi_x.Init(num_gauss, num_points); - weights.Copy(omega()); - x.Init(data.n_rows()); - identity_vector.Init(num_points); - identity_vector.SetAll(1); - - if(g_fit != NULL) { - g_mu.Init(num_gauss); - g_sigma.Init(num_gauss); - } - else { - g_mu.Init(0); - g_sigma.Init(0); - g_omega.Init(0); - tmp_g_omega.Init(0); - } - - for(index_t k = 0; k < num_gauss; k++) { - if (g_fit != NULL) { - g_mu[k].Init(dim); - g_mu[k].SetZero(); - g_sigma[k].Init((dim * (dim+1) / 2)); - g_sigma[k].SetZero(); - } - for(index_t i = 0; i < num_points; i++) { - if (g_fit != NULL) { - Vector tmp_g_mu, tmp_g_sigma; - x.CopyValues(data.GetColumnPtr(i)); - tmpVal = phi(x, mu(k), sigma(k), - d_sigma(k), &tmp_g_mu, &tmp_g_sigma); - phi_x.set(k, i, tmpVal); - la::AddTo(tmp_g_mu, &g_mu[k]); - la::AddTo(tmp_g_sigma, &g_sigma[k]); - } - else { - x.CopyValues(data.GetColumnPtr(i)); - phi_x.set(k, i, phi(x, mu(k), sigma(k))); - } - } - if (g_fit != NULL) { - la::Scale(weights.get(k), &g_mu[k]); - la::Scale(weights.get(k), &g_sigma[k]); - } - } - - la::MulInit(weights, phi_x, &y); - fit = la::Dot(y, identity_vector); - - if (g_fit != NULL) { - // Calculating the g_omega - la::MulInit(phi_x, identity_vector, &tmp_g_omega); - la::MulInit(d_omega(), tmp_g_omega, &g_omega); - - // Making the single gradient vector of size K*(D+1)*(D+2)/2 - double *tmp_g_fit; - tmp_g_fit = (double*)malloc(((num_gauss * (dim+1)*(dim+2) / 2) - 1) - *sizeof(double)); - index_t j = 0; - for(index_t k = 0; k < g_omega.length(); k++) - tmp_g_fit[k] = g_omega.get(k); - j = g_omega.length(); - for(index_t k = 0; k < num_gauss; k++){ - for(index_t i = 0; i < dim; i++){ - tmp_g_fit[j + k*dim + i] = g_mu[k].get(i); - } - for(index_t i = 0; i < (dim * (dim+1) / 2); i++){ - tmp_g_fit[j + num_gauss*dim - + k*(dim * (dim+1) / 2) - + i] = g_sigma[k].get(i); - } - } - g_fit->Copy(tmp_g_fit, ((num_gauss*(dim+1)*(dim+2) / 2) - 1)); - } - return fit; -} - -void MoGL2E::MultiplePointsGenerator(double **points, - index_t number_of_points, - const Matrix& d, - index_t number_of_components) { - - index_t dim, n, i, j, x; - - dim = d.n_rows(); - n = d.n_cols(); - - for( i = 0; i < number_of_points; i++) { - for(j = 0; j < number_of_components - 1; j++) { - points[i][j] = (rand() % 20001)/1000 - 10; - } - } - - for(i = 0; i < number_of_points; i++){ - for(j = 0; j < number_of_components; j++){ - Vector tmp_mu; - tmp_mu.Init(dim); - tmp_mu.CopyValues(d.GetColumnPtr((rand() % n))); - for(x = 0; x < dim; x++) - points[i][number_of_components - 1 + j * dim + x] = tmp_mu.get(x); - } - } - - for(i = 0; i < number_of_points; i++) - for(j = 0; j < number_of_components; j++) - for(x = 0 ; x < (dim * (dim + 1) / 2); x++) - points[i][(number_of_components * (dim + 1) - 1) - + (j * (dim * (dim + 1) / 2)) + x] = (rand() % 501)/100; - - return; -} - -void MoGL2E::InitialPointGenerator(double *theta, const Matrix& data, - index_t k_comp) { - - ArrayList means; - ArrayList covars; - Vector weights; - double temp, noise; - index_t dim; - - weights.Init(k_comp); - means.Init(k_comp); - covars.Init(k_comp); - dim = data.n_rows(); - - for (index_t i = 0; i < k_comp; i++) { - means[i].Init(dim); - covars[i].Init(dim, dim); - } - - KMeans_(data, &means, &covars, &weights, k_comp); - - for(index_t k = 0; k < k_comp - 1; k++){ - temp = weights[k] / weights[k_comp - 1]; - noise = (double)(rand() % 10000) / (double)1000; - theta[k] = noise - 5; - } - for(index_t k = 0; k < k_comp; k++){ - for(index_t j = 0; j < dim; j++) - theta[k_comp - 1 + k * dim + j] = means[k].get(j); - - Matrix U, U_tran; - la::CholeskyInit(covars[k], &U); - la::TransposeInit(U, &U_tran); - for(index_t j = 0; j < dim; j++) { - for(index_t i = 0; i < j + 1; i++) { - noise = (rand() % 501) / 100; - theta[k_comp - 1 + k_comp * dim - + k * dim * (dim + 1) / 2 - + j * (j + 1) / 2 + i] = U_tran.get(j, i) + noise; - } - } - } - return; -} - -void MoGL2E::KMeans_(const Matrix& data, ArrayList *means, - ArrayList *covars, Vector *weights, - index_t value_of_k){ - - ArrayList mu, mu_old; - double* tmpssq; - double* sig; - double* sig_best; - index_t *y; - Vector x, diff; - Matrix ssq; - index_t i, j, k, n, t, dim; - double score, score_old, sum; - - n = data.n_cols(); - dim = data.n_rows(); - mu.Init(value_of_k); - mu_old.Init(value_of_k); - tmpssq = (double*)malloc(value_of_k * sizeof( double )); - sig = (double*)malloc(value_of_k * sizeof( double )); - sig_best = (double*)malloc(value_of_k * sizeof( double )); - ssq.Init(n, value_of_k); - - for( i = 0; i < value_of_k; i++){ - mu[i].Init(dim); - mu_old[i].Init(dim); - } - x.Init(dim); - y = (index_t*)malloc(n * sizeof(index_t)); - diff.Init(dim); - - score_old = 999999; - - // putting 5 random restarts to obtain the k-means - for(i = 0; i < 5; i++){ - t = -1; - for (k = 0; k < value_of_k; k++){ - t = (t + 1 + (rand()%((n - 1 - (value_of_k - k)) - (t + 1)))); - mu[k].CopyValues(data.GetColumnPtr(t)); - for(j = 0; j < n; j++){ - x.CopyValues( data.GetColumnPtr(j)); - la::SubOverwrite(mu[k], x, &diff); - ssq.set( j, k, la::Dot(diff, diff)); - } - } - min_element(ssq, y); - - do{ - for(k = 0; k < value_of_k; k++){ - mu_old[k].CopyValues(mu[k]); - } - - for(k = 0; k < value_of_k; k++){ - index_t p = 0; - mu[k].SetZero(); - for(j = 0; j < n; j++){ - x.CopyValues(data.GetColumnPtr(j)); - if(y[j] == k){ - la::AddTo(x, &mu[k]); - p++; - } - } - - if(p == 0){ - } - else{ - double sc = 1 ; - sc = sc / p; - la::Scale(sc , &mu[k]); - } - for(j = 0; j < n; j++){ - x.CopyValues(data.GetColumnPtr(j)); - la::SubOverwrite(mu[k], x, &diff); - ssq.set(j, k, la::Dot(diff, diff)); - } - } - min_element(ssq, y); - - sum = 0; - for(k = 0; k < value_of_k; k++) { - la::SubOverwrite(mu[k], mu_old[k], &diff); - sum += la::Dot(diff, diff); - } - }while(sum != 0); - - for(k = 0; k < value_of_k; k++){ - index_t p = 0; - tmpssq[k] = 0; - for(j = 0; j < n; j++){ - if(y[j] == k){ - tmpssq[k] += ssq.get(j, k); - p++; - } - } - sig[k] = sqrt(tmpssq[k] / p); - } - - score = 0; - for(k = 0; k < value_of_k; k++){ - score += tmpssq[k]; - } - score = score / n; - - if (score < score_old) { - score_old = score; - for(k = 0; k < value_of_k; k++){ - (*means)[k].CopyValues(mu[k]); - sig_best[k] = sig[k]; - } - } - } - - for(k = 0; k < value_of_k; k++){ - x.SetAll(sig_best[k]); - (*covars)[k].SetDiagonal(x); - } - double tmp = 1; - weights->SetAll(tmp / value_of_k); - return; -} - -void MoGL2E::min_element( Matrix& element, index_t *indices ){ - - index_t last = element.n_cols() - 1; - index_t first, lowest; - index_t i; - - for( i = 0; i < element.n_rows(); i++ ){ - - first = lowest = 0; - if(first == last){ - indices[ i ] = last; - } - while(++first <= last){ - if( element.get( i , first ) < element.get( i , lowest ) ){ - lowest = first; - } - } - indices[ i ] = lowest; - } - return; -} - diff --git a/fastlib/u/pram/mog_l2e/faster/mog.h b/fastlib/u/pram/mog_l2e/faster/mog.h deleted file mode 100644 index 477d39054e..0000000000 --- a/fastlib/u/pram/mog_l2e/faster/mog.h +++ /dev/null @@ -1,595 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file mog.h - * - * Defines a Gaussian Mixture model and - * estimates the parameters of the model - * - */ - -#ifndef MOGL2E_H -#define MOGL2E_H - -#include - - -/** - * A Gaussian mixture model class. - * - * This class uses L2 loss function to - * estimate the parameters of a gaussian mixture - * model on a given data. - * - * The parameters are converted for optimization - * to maintain the following facts: - * - the weights sum to one - * - for this, the weights were parameterized using - * the logistic function - * - the covariance matrix is always positive definite - * - for this, the Cholesky decomposition is used - * - * Example use: - * - * @code - * MoGL2E mog; - * ArrayList results; - * double *params; - * - * mog.MakeModel(number_of_gaussians, dimension, params); - * mog.L2Error(data); - * mog.OutputResults(&results); - * @endcode - */ -class MoGL2E { - - private: - - // The parameters of the Mixture model - ArrayList mu_; - ArrayList sigma_; - Vector omega_; - index_t number_of_gaussians_; - index_t dimension_; - - // The differential for the paramterization - // for optimization - Matrix d_omega_; - ArrayList > d_sigma_; - - public: - - MoGL2E() { - mu_.Init(0); - sigma_.Init(0); - d_sigma_.Init(0); - d_omega_.Init(0, 0); - } - - ~MoGL2E() { - } - - void Init(index_t num_gauss, index_t dimension) { - - // Destruct everything to initialize afresh - mu_.Clear(); - sigma_.Clear(); - d_sigma_.Clear(); - // Initialize the private variables - number_of_gaussians_ = num_gauss; - dimension_ = dimension; - - // Resize the ArrayList of Vectors and Matrices - mu_.Resize(number_of_gaussians_); - sigma_.Resize(number_of_gaussians_); - } - - void Resize_d_sigma_() { - d_sigma_.Resize(number_of_gaussians()); - for(index_t i =0; i < number_of_gaussians(); i++) { - d_sigma_[i].Init(dimension()*(dimension()+1)/2); - } - } - - /** - * - * This function uses the parameters used for optimization - * and converts it into athe parameters of a Gaussian - * mixture model. This is to be used when you do not want - * the gradient values. - * - * Example use: - * - * @code - * MoGL2E mog; - * mog.MakeModel(number_of_gaussians, dimension, - * parameters_for_optimization); - * @endcode - */ - - void MakeModel(index_t num_mods, index_t dimension, double* theta) { - double *temp_mu; - Matrix lower_triangle_matrix, upper_triangle_matrix; - double sum, s_min = 0.01; - - Init(num_mods, dimension); - temp_mu = (double*) malloc (dimension * sizeof(double)) ; - lower_triangle_matrix.Init(dimension, dimension); - upper_triangle_matrix.Init(dimension, dimension); - - // calculating the omega values - sum = 0; - double *temp_array; - temp_array = (double*) malloc (num_mods * sizeof(double)); - for(index_t i = 0; i < num_mods - 1; i++) { - temp_array[i] = exp(theta[i]) ; - sum += temp_array[i] ; - } - temp_array[num_mods - 1] = 1 ; - ++sum ; - la::Scale(num_mods, (1.0 / sum), temp_array); - set_omega(dimension, temp_array); - - // calculating the mu values - for(index_t k = 0; k < num_mods; k++) { - for(index_t j = 0; j < dimension; j++) { - temp_mu[j] = theta[num_mods + k*dimension + j - 1]; - } - set_mu(k, dimension, temp_mu); - } - - // calculating the sigma values - // using a lower triangular matrix and its transpose - // to obtain a positive definite symmetric matrix - Matrix sigma_temp; - sigma_temp.Init(dimension, dimension); - for(index_t k = 0; k < num_mods; k++) { - lower_triangle_matrix.SetAll(0.0); - for(index_t j = 0; j < dimension; j++) { - for(index_t i = 0; i < j; i++) { - lower_triangle_matrix.set(j, i, - theta[(num_mods - 1) - + num_mods*dimension - + k*(dimension*(dimension + 1) / 2) - + (j*(j + 1) / 2) + i]); - } - lower_triangle_matrix.set(j, j, - theta[(num_mods - 1) - + num_mods*dimension - + k*(dimension*(dimension + 1) / 2) - + (j*(j + 1) / 2) + j] + s_min); - - } - la::TransposeOverwrite(lower_triangle_matrix, &upper_triangle_matrix); - la::MulOverwrite(lower_triangle_matrix, upper_triangle_matrix, &sigma_temp); - set_sigma(k, sigma_temp); - } - } - - void MakeModel(datanode *mog_l2e_module, double* theta) { - index_t num_gauss = fx_param_int_req(mog_l2e_module, "K"); - index_t dimension = fx_param_int_req(mog_l2e_module, "D"); - MakeModel(num_gauss, dimension, theta); - } - - /** - * - * This function uses the parameters used for optimization - * and converts it into athe parameters of a Gaussian - * mixture model. This is to be used when you want - * the gradient values. - * - * Example use: - * - * @code - * MoGL2E mog; - * mog.MakeModelWithGradients(number_of_gaussians, dimension, - * parameters_for_optimization); - * @endcode - */ - - void MakeModelWithGradients(index_t num_mods, index_t dimension, double* theta) { - double *temp_mu; - Matrix lower_triangle_matrix, upper_triangle_matrix; - double sum, s_min = 0.01; - - Init(num_mods, dimension); - temp_mu = (double*) malloc (dimension * sizeof(double)); - lower_triangle_matrix.Init(dimension, dimension); - upper_triangle_matrix.Init(dimension, dimension); - - // calculating the omega values - sum = 0; - double *temp_array; - temp_array = (double*) malloc (num_mods * sizeof(double)); - for(index_t i = 0; i < num_mods - 1; i++) { - temp_array[i] = exp(theta[i]) ; - sum += temp_array[i] ; - } - temp_array[num_mods - 1] = 1 ; - ++sum ; - la::Scale(num_mods, (1.0 / sum), temp_array); - set_omega(num_mods, temp_array); - - // calculating the d_omega values - Matrix d_omega_temp; - d_omega_temp.Init(num_mods - 1, num_mods); - d_omega_temp.SetAll(0.0); - for(index_t i = 0; i < num_mods - 1; i++) { - for(index_t j = 0; j < i; j++) { - d_omega_temp.set(i,j,-(omega(i)*omega(j))); - d_omega_temp.set(j,i,-(omega(i)*omega(j))); - } - d_omega_temp.set(i,i,omega(i)*(1-omega(i))); - } - for(index_t i = 0; i < num_mods - 1; i++) { - d_omega_temp.set(i, num_mods - 1, -(omega(i)*omega(num_mods - 1))); - } - set_d_omega(d_omega_temp); - - // calculating the mu values - for(index_t k = 0; k < num_mods; k++) { - for(index_t j = 0; j < dimension; j++) { - temp_mu[j] = theta[num_mods + k*dimension + j - 1]; - } - set_mu(k, dimension, temp_mu); - } - // d_mu is not computed because it is implicitly known - // since no parameterization is applied on them - - // using a lower triangular matrix and its transpose - // to obtain a positive definite symmetric matrix - - // initializing the d_sigma values - - Matrix d_sigma_temp; - d_sigma_temp.Init(dimension, dimension); - Resize_d_sigma_(); - - // calculating the sigma values - Matrix sigma_temp; - sigma_temp.Init(dimension, dimension); - for(index_t k = 0; k < num_mods; k++) { - lower_triangle_matrix.SetAll(0.0); - for(index_t j = 0; j < dimension; j++) { - for(index_t i = 0; i < j; i++) { - lower_triangle_matrix.set( j, i, - theta[(num_mods - 1) - + num_mods*dimension - + k*(dimension*(dimension + 1) / 2) - + (j*(j + 1) / 2) + i]) ; - } - lower_triangle_matrix.set(j, j, - theta[(num_mods - 1) - + num_mods*dimension - + k*(dimension*(dimension + 1) / 2) - + (j*(j + 1) / 2) + j] + s_min); - } - la::TransposeOverwrite(lower_triangle_matrix, &upper_triangle_matrix); - la::MulOverwrite(lower_triangle_matrix, upper_triangle_matrix, &sigma_temp); - set_sigma(k, sigma_temp); - - // calculating the d_sigma values - for(index_t i = 0; i < dimension; i++){ - for(index_t in = 0; in < i+1; in++){ - Matrix d_sigma_d_r,d_sigma_d_r_t,temp_matrix_1,temp_matrix_2; - d_sigma_d_r.Init(dimension, dimension); - d_sigma_d_r_t.Init(dimension, dimension); - d_sigma_d_r.SetAll(0.0); - d_sigma_d_r_t.SetAll(0.0); - d_sigma_d_r.set(i,in,1.0); - d_sigma_d_r_t.set(in,i,1.0); - - la::MulInit(d_sigma_d_r,upper_triangle_matrix,&temp_matrix_1); - la::MulInit(lower_triangle_matrix,d_sigma_d_r_t,&temp_matrix_2); - la::AddOverwrite(temp_matrix_1,temp_matrix_2,&d_sigma_temp); - set_d_sigma(k, (i*(i+1)/2)+in, d_sigma_temp); - } - } - } - } - - ////// THE GET FUNCTIONS ////// - ArrayList& mu() { - return mu_; - } - - ArrayList& sigma() { - return sigma_; - } - - Vector& omega() { - return omega_; - } - - index_t number_of_gaussians() { - return number_of_gaussians_; - } - - index_t dimension() { - return dimension_; - } - - Vector& mu(index_t i) { - return mu_[i] ; - } - - Matrix& sigma(index_t i) { - return sigma_[i]; - } - - double omega(index_t i) { - return omega_.get(i); - } - - Matrix& d_omega(){ - return d_omega_; - } - - ArrayList >& d_sigma(){ - return d_sigma_; - } - - ArrayList& d_sigma(index_t i){ - return d_sigma_[i]; - } - - ////// THE SET FUNCTIONS ////// - - void set_mu(index_t i, Vector& mu) { - DEBUG_ASSERT(i < number_of_gaussians()); - DEBUG_ASSERT(mu.length() == dimension()); - mu_[i].Copy(mu); - return; - } - - void set_mu(index_t i, index_t length, const double *mu) { - DEBUG_ASSERT(i < number_of_gaussians()); - DEBUG_ASSERT(length == dimension()); - mu_[i].Copy(mu, length); - return; - } - - void set_sigma(index_t i, Matrix& sigma) { - DEBUG_ASSERT(i < number_of_gaussians()); - DEBUG_ASSERT(sigma.n_rows() == dimension()); - DEBUG_ASSERT(sigma.n_cols() == dimension()); - sigma_[i].Copy(sigma); - return; - } - - void set_omega(Vector& omega) { - DEBUG_ASSERT(omega.length() == number_of_gaussians()); - omega_.Copy(omega); - return; - } - - void set_omega(index_t length, const double *omega) { - DEBUG_ASSERT(length == number_of_gaussians()); - omega_.Copy(omega, length); - return; - } - - void set_d_omega(Matrix& d_omega) { - d_omega_.Destruct(); - d_omega_.Copy(d_omega); - return; - } - - void set_d_sigma(index_t i, index_t j, Matrix& d_sigma_i_j) { - d_sigma_[i][j].Copy(d_sigma_i_j); - return; - } - - /** - * This function outputs the parameters of the model - * to an arraylist of doubles - * - * @code - * ArrayList results; - * mog.OutputResults(&results); - * @endcode - */ - void OutputResults(ArrayList *results) { - - // Initialize the size of the output array - (*results).Init(number_of_gaussians_ * (1 + dimension_*(1 + dimension_))); - - // Copy values to the array from the private variables of the class - for (index_t i = 0; i < number_of_gaussians_; i++) { - (*results)[i] = omega(i); - for (index_t j = 0; j < dimension_; j++) { - (*results)[number_of_gaussians_ + i*dimension_ + j] = mu(i).get(j); - for (index_t k = 0; k < dimension_; k++) { - (*results)[number_of_gaussians_*(1 + dimension_) - + i*dimension_*dimension_ + j*dimension_ - + k] = sigma(i).get(j, k); - } - } - } - } - - /** - * This function prints the parameters of the model - * - * @code - * mog.Display(); - * @endcode - */ - void Display(){ - - // Output the model parameters as the omega, mu and sigma - printf(" Omega : [ "); - for (index_t i = 0; i < number_of_gaussians_; i++) { - printf("%lf ", omega(i)); - } - printf("]\n"); - printf(" Mu : \n["); - for (index_t i = 0; i < number_of_gaussians_; i++) { - for (index_t j = 0; j < dimension_ ; j++) { - printf("%lf ", mu(i).get(j)); - } - printf(";"); - if (i == (number_of_gaussians_ - 1)) { - printf("\b]\n"); - } - } - printf("Sigma : "); - for (index_t i = 0; i < number_of_gaussians_; i++) { - printf("\n["); - for (index_t j = 0; j < dimension_ ; j++) { - for(index_t k = 0; k < dimension_ ; k++) { - printf("%lf ",sigma(i).get(j, k)); - } - printf(";"); - } - printf("\b]"); - } - printf("\n"); - } - - - /** - * This function calculates the L2 error and - * the gradient of the error with respect to the - * parameters given the data and the parameterized - * mixture - * - * Example use: - * - * @code - * const Matrix data; - * MoGL2E mog; - * index_t num_gauss, dimension; - * double *params; // get the parameters - * - * mog.MakeModel(num_gauss, dimension, params); - * mog.L2Error(data); - * @endcode - */ - long double L2Error(const Matrix&, Vector* = NULL); - - /** - * Calculates the regularization value for a - * Gaussian mixture and its gradient with - * respect to the parameters - * - * Used by the 'L2Error' function to calculate - * the regularization part of the error - */ - long double RegularizationTerm_(Vector* = NULL); - - /** - * Calculates the goodness-of-fit value for a - * Gaussian mixture and its gradient with - * respect to the parameters - * - * Used by the 'L2Error' function to calculate - * the goodness-of-fit part of the error - */ - long double GoodnessOfFitTerm_(const Matrix&, Vector* = NULL); - - /** - * This function computes multiple number of starting points - * required for the Nelder Mead method - * - * Example use: - * @code - * double **p; - * index_t n, num_gauss; - * const Matrix data; - * - * MoGL2E::MultiplePointsGeneratot(p, n, data, num_gauss); - * @endcode - */ - static void MultiplePointsGenerator(double**, index_t, - const Matrix&, index_t); - - /** - * This function parameterizes the starting point obtained - * from the 'k_means" for optimization purposes using the - * Quasi Newton method - * - * Example use: - * @code - * double *p; - * index_t num_gauss; - * const Matrix data; - * - * MoGL2E::InitialPointGeneratot(p, data, num_gauss); - * @endcode - */ - static void InitialPointGenerator(double*, const Matrix&, index_t); - - /** - * This function computes the k-means of the data and stores - * the calculated means and covariances in the ArrayList - * of Vectors and Matrices passed to it. It sets the weights - * uniformly. - * - * This function is used to obtain a starting point for - * the optimization - * - * Example use: - * - * @code - * const Matrix data; - * ArrayList *means; - * ArrayList *covars; - * Vector *weights; - * index_t num_gauss; - * - * ... - * MoGL2E::KMeans(data, means, covars, weights, num_gauss); - *@endcode - */ - static void KMeans_(const Matrix&, ArrayList*, - ArrayList*, Vector*, index_t); - - /** - * This function returns the indices of the minimum - * element in each row of a matrix - */ - static void min_element(Matrix&, index_t*); - - /** - * This is the function which would be used for - * optimization. It creates its own object of - * class MoGL2E and returns the L2 error - * and the gradient which are computed by - * the functions of the class - * - */ - static long double L2ErrorForOpt(Vector& params, - const Matrix& data, - Vector *gradient) { - - MoGL2E model; - index_t dimension = data.n_rows(); - index_t num_gauss; - - num_gauss = (params.length() + 1)*2 / ((dimension+1)*(dimension+2)); - // This check added here to see if - // the gradient is actually demanded here - if (gradient != NULL) { - model.MakeModelWithGradients(num_gauss, dimension, params.ptr()); - return model.L2Error(data, gradient); - } - else { - model.MakeModel(num_gauss, dimension, params.ptr()); - return model.L2Error(data); - } - } - - /** - * This is the function which should be used for - * optimization when there is no need to compute - * any gradients - * - */ - static long double L2ErrorForOpt(Vector& params, const Matrix& data) { - return L2ErrorForOpt(params, data, NULL); - } - -}; - -#endif diff --git a/fastlib/u/pram/mog_l2e/faster/mog_l2e_c_expt_data.csv b/fastlib/u/pram/mog_l2e/faster/mog_l2e_c_expt_data.csv deleted file mode 100644 index ecf2d67940..0000000000 --- a/fastlib/u/pram/mog_l2e/faster/mog_l2e_c_expt_data.csv +++ /dev/null @@ -1,1000 +0,0 @@ --0.6165,-0.7845,-0.94595 --2.3738,-1.5512,-2.151 -0.17863,0.15363,0.11381 -0.41,0.2685,0.47045 --1.634,-1.8045,-1.284 -1.6973,1.0077,1.1101 -1.6948,1.2375,1.1921 --0.053636,0.075703,-0.16084 -0.46646,0.37428,0.88056 -0.2489,0.15923,0.10354 --0.2661,-0.67076,-0.74904 -1.0344,0.81231,0.65549 --0.83848,-0.6146,-0.75169 -3.1115,2.2803,2.1457 --0.19439,0.049159,-0.18603 -0.16238,0.085079,-0.14036 -1.5204,1.1411,0.57399 -0.084489,-0.044184,-0.10485 --0.13632,-0.19165,-0.57118 --1.1863,-0.92949,-0.87861 -0.4196,-0.0028355,0.45413 --1.9044,-1.234,-0.59965 -1.0181,1.074,0.97021 -2.3139,1.4096,1.6465 --0.98593,-0.82848,-0.42251 -1.2228,1.0583,1.0334 -1.7872,1.2369,1.6017 --2.2714,-1.0872,-1.457 --2.0537,-1.9257,-1.539 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--3.9557,0.16992,1.9112 --1.0379,1.8161,3.9284 --1.9193,1.0014,3.342 --0.11416,2.2954,4.2907 --2.3313,0.96534,2.8068 --1.2659,1.4917,3.8215 --2.9292,0.12168,2.6602 --1.98,0.6965,2.9268 --0.27129,2.2757,4.0148 -0.010834,2.6759,4.4826 --1.8767,1.4824,3.2742 -1.6277,3.7608,5.7578 --2.1222,0.69342,1.8976 --0.16599,2.5066,4.7662 --1.647,0.93619,2.632 --2.4732,0.63763,2.4119 -0.62836,2.3891,4.2134 --1.5908,1.6857,3.1838 --2.3837,0.87428,2.3988 --2.2597,1.1045,3.0302 --1.5337,1.2809,3.7214 --2.009,1.0864,2.7673 --1.9083,0.73419,2.7602 --2.2399,0.43803,2.3513 --3.3707,-0.25379,2.158 --2.0399,1.5221,2.8082 --4.1977,-0.39451,1.5023 --2.2258,0.74821,2.5628 --1.8701,0.93563,2.9273 --1.5203,1.0059,3.3122 --0.69018,2.2939,4.2232 --3.6696,0.15431,1.5653 --3.7288,-0.046977,1.5477 --3.8497,-9.0309,9.8855 --0.76216,5.5497,5.6213 -6.5951,1.0681,7.0362 --3.9972,-8.645,3.2122 -4.1924,-0.07647,-4.5577 --8.508,6.7221,-5.1831 -5.0043,-0.78132,-1.4815 --0.62189,5.6005,1.9512 -8.8983,-0.16512,-1.5992 --9.7205,3.9143,5.3505 --8.916,9.7012,-9.4862 -4.769,-2.9366,3.8559 -2.7113,2.4398,5.0688 -8.6194,0.31788,9.0712 --0.78079,6.3006,-2.2536 -0.2578,-0.21872,-8.8769 --1.5104,4.6292,-2.4932 --1.6817,4.2115,3.1503 --2.2353,6.6882,4.0353 -7.1107,-9.7815,-8.1853 -6.4604,-9.0325,8.9597 -8.2782,0.025347,0.69698 -6.7208,-4.1662,8.2048 -7.3618,-9.7241,-3.8138 -5.5117,5.8836,4.4979 --1.3614,-8.6543,-2.3308 --1.278,-3.7964,7.9775 --5.5139,-3.1999,-8.6312 --9.7363,6.3356,-4.7 --2.4807,-9.9125,2.4912 -6.7712,-0.12368,3.6056 --4.2751,2.269,4.738 --4.3174,-2.5184,8.6596 --3.4584,-6.0338,2.2934 --0.47581,7.9014,4.7661 --5.4803,9.737,2.7305 -9.2401,8.4356,-6.5973 --3.8708,-2.1117,-4.5533 -5.5785,3.2457,6.249 -7.9068,0.8022,-1.9348 --4.9255,-5.7784,3.2017 --7.0849,-3.8934,-4.2788 --3.1701,-9.732,9.2277 -4.6627,-9.9088,1.5311 --9.3227,9.7557,9.3326 -0.88478,6.2359,0.27842 -1.1689,5.4081,7.2845 -3.3937,8.0657,-5.4934 -1.6785,-7.5245,-5.1309 -3.7182,-8.0784,7.9765 -5.3197,1.0294,4.184 -7.9532,-9.8696,9.5029 -9.3358,-7.4861,-0.80871 -8.1159,-8.8844,4.8355 --6.4284,5.2535,3.3793 -9.5698,-8.572,-9.4078 -7.9597,4.3647,-2.9929 --9.4128,-7.1918,-8.3587 --4.3596,2.314,-8.7833 --6.242,-0.56982,5.2019 -2.1751,0.20705,2.5566 --5.8129,-3.0991,0.60934 -9.9547,-8.3048,9.7852 --9.6648,-4.884,-3.9408 --1.2074,0.51144,-6.386 --7.0321,-7.1893,1.385 -2.4289,-7.5906,-0.66856 -5.0874,0.054778,-2.1554 --4.3881,-4.2817,-9.4519 -0.54359,-7.7607,-3.1704 --3.7512,0.00092528,9.527 --6.3652,-2.442,1.5387 -5.5851,-4.5888,5.4128 -6.1902,9.1338,2.5282 -4.4663,-0.83864,4.661 --9.0938,-5.0996,-7.0132 -2.266,6.6091,-1.7072 --3.2957,-3.9365,-8.6203 -1.6773,-9.3356,7.6839 -8.2636,9.7512,6.6399 -0.02545,-0.40447,-3.6958 --1.6498,2.0707,7.9611 -5.9892,0.32498,5.062 -7.8122,9.5452,-6.949 -0.26707,-5.056,4.3083 --2.2055,3.3639,6.8328 -8.6128,-7.0654,5.6692 --4.3454,-1.8976,9.9566 -5.3987,-7.1589,1.7223 -4.5517,-3.1151,-2.7844 -8.2922,0.99864,-2.5218 --7.8776,-9.1514,9.7444 -7.7788,-5.3608,7.7442 -1.9008,-2.0242,-6.2874 --9.7584,-7.5862,-5.1174 --5.2337,-1.1474,-5.0626 --7.5275,-8.971,-6.5237 -7.6157,9.9329,-3.3139 -6.2646,3.8875,-1.8755 -5.2668,2.3004,2.4755 -6.2077,2.3431,7.9601 -3.6451,6.5948,3.5833 -2.496,9.5857,-7.3691 --2.6384,-1.2513,-0.54221 --5.9444,6.9242,9.349 --4.499,-2.0583,-0.024743 --0.36912,-1.5926,-2.0584 --1.9962,7.8398,9.7645 -4.0459,-3.0868,5.3666 --3.5728,6.1683,9.6629 diff --git a/fastlib/u/pram/mog_l2e/faster/mog_l2e_main.cc b/fastlib/u/pram/mog_l2e/faster/mog_l2e_main.cc deleted file mode 100644 index da4fd47981..0000000000 --- a/fastlib/u/pram/mog_l2e/faster/mog_l2e_main.cc +++ /dev/null @@ -1,145 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file mog_l2e_main.cc - * - * This program test drives the L2 estimation - * of a Gaussian Mixture model. - * - * PARAMETERS TO BE INPUT: - * - * --data - * This is the file that contains the data on which - * the model is to be fit - * - * --mog_l2e/K - * This is the number of gaussians we want to fit - * on the data, defaults to '1' - * - * --output - * This file will contain the parameters estimated, - * defaults to 'output.csv' - * - */ - -#include "mog.h" -#include "../opt/optimizers.h" -//#include - - -int main(int argc, char* argv[]) { - - fx_init(argc, argv); - //srand(time(NULL)); - ////// READING PARAMETERS AND LOADING DATA ////// - - const char *data_filename = fx_param_str_req(NULL, "data"); - - Matrix data_points; - data::Load(data_filename, &data_points); - - ////// MIXTURE OF GAUSSIANS USING L2 ESTIMATION ////// - - datanode *mog_l2e_module = fx_submodule(NULL, "mog_l2e", "mog_l2e"); - index_t number_of_gaussians = fx_param_int(mog_l2e_module, "K", 1); - fx_format_param(mog_l2e_module, "D", "%d", data_points.n_rows()); - index_t dimension = fx_param_int_req(mog_l2e_module, "D");; - - ////// RUNNING AN OPTIMIZER TO MINIMIZE THE L2 ERROR ////// - - datanode *opt_module = fx_submodule(NULL, "opt", "opt"); - const char *opt_method = fx_param_str(opt_module, "method", "QuasiNewton"); - index_t param_dim = (number_of_gaussians*(dimension+1)*(dimension+2)/2 - 1); - fx_param_int(opt_module, "param_space_dim", param_dim); - - index_t optim_flag = (strcmp(opt_method, "NelderMead") == 0 ? 1 : 0); - MoGL2E mog; - - - if (optim_flag == 1) { - - ////// OPTIMIZER USING NELDER MEAD METHOD ////// - - NelderMead opt; - - ////// Initializing the optimizer ////// - fx_timer_start(opt_module, "init_opt"); - opt.Init(MoGL2E::L2ErrorForOpt, data_points, opt_module); - fx_timer_stop(opt_module, "init_opt"); - - ////// Getting starting points for the optimization ////// - double **pts; - pts = (double**)malloc((param_dim+1)*sizeof(double*)); - for(index_t i = 0; i < param_dim+1; i++) { - pts[i] = (double*)malloc(param_dim*sizeof(double)); - } - - fx_timer_start(opt_module, "get_init_pts"); - MoGL2E::MultiplePointsGenerator(pts, param_dim+1, - data_points, number_of_gaussians); - fx_timer_stop(opt_module, "get_init_pts"); - - ////// The optimization ////// - - fx_timer_start(opt_module, "optimizing"); - opt.Eval(pts); - fx_timer_stop(opt_module, "optimizing"); - - ////// Making model with the optimal parameters ////// - mog.MakeModel(mog_l2e_module, pts[0]); - - } - else { - - ////// OPTIMIZER USING QUASI NEWTON METHOD ////// - - QuasiNewton opt; - - ////// Initializing the optimizer ////// - fx_timer_start(opt_module, "init_opt"); - opt.Init(MoGL2E::L2ErrorForOpt, data_points, opt_module); - fx_timer_stop(opt_module, "init_opt"); - - ////// Getting starting point for the optimization ////// - double *pt; - pt = (double*)malloc(param_dim*sizeof(double)); - - //index_t rs = 0; - //while ( rs < 5) { - - fx_timer_start(opt_module, "get_init_pt"); - MoGL2E::InitialPointGenerator(pt, data_points, number_of_gaussians); - fx_timer_stop(opt_module, "get_init_pt"); - - ////// The optimization ////// - - fx_timer_start(opt_module, "optimizing"); - opt.Eval(pt); - fx_timer_stop(opt_module, "optimizing"); - - ////// Making model with optimal parameters ////// - mog.MakeModel(mog_l2e_module, pt); - //printf("minimum achieved: %Lf\n", mog.L2Error(data_points)); - //} - - } - - long double error = mog.L2Error(data_points); - NOTIFY("Minimum L2 error achieved: %Lf", error); - mog.Display(); - - ArrayList results; - mog.OutputResults(&results); - - - ////// OUTPUT RESULTS ////// - - const char *output_filename = fx_param_str(NULL, "output", "output.csv"); - - FILE *output_file = fopen(output_filename, "w"); - - ot::Print(results, output_file); - fclose(output_file); - fx_done(); - - return 1; -} diff --git a/fastlib/u/pram/mog_l2e/faster/phi.h b/fastlib/u/pram/mog_l2e/faster/phi.h deleted file mode 100644 index bdddb8720f..0000000000 --- a/fastlib/u/pram/mog_l2e/faster/phi.h +++ /dev/null @@ -1,156 +0,0 @@ -/** - * @author Parikshit Ram (pram@cc.gatech.edu) - * @file phi.h - * - * This file computes the Gaussian probability - * density function - */ -#include "fastlib/fastlib.h" -#include "fastlib/fastlib_int.h" -#include - -/** - * Calculates the multivariate Gaussian probability density function - * - * Example use: - * @code - * Vector x, mean; - * Matrix cov; - * .... - * long double f = phi(x, mean, cov); - * @endcode - */ - -long double phi(Vector& x , Vector& mean , Matrix& cov) { - - long double det, f; - double exponent; - index_t dim; - Matrix inv; - Vector diff, tmp; - - dim = x.length(); - la::InverseInit(cov, &inv); - det = la::Determinant(cov); - - if( det < 0){ - det = -det; - } - la::SubInit(mean,x,&diff); - la::MulInit(inv, diff, &tmp); - exponent = la::Dot(diff, tmp); - long double tmp1, tmp2, tmp3; - tmp1 = 1; - tmp2 = dim; - tmp2 = tmp2/2; - tmp2 = pow((2*(math::PI)),tmp2); - tmp1 = tmp1/tmp2; - tmp3 = 1; - tmp2 = sqrt(det); - tmp3 = tmp3/tmp2; - tmp2 = -exponent; - tmp2 = tmp2 / 2; - f = (tmp1*tmp3*exp(tmp2)); - - return f; -} - -/** - * Calculates the univariate Gaussian probability density function - * - * Example use: - * @code - * double x, mean, var; - * .... - * long double f = phi(x, mean, var); - * @endcode - */ - -long double phi(double x, double mean, double var) { - - long double f; - - f = exp(-1.0*((x-mean)*(x-mean)/(2*var)))/sqrt(2*math::PI*var); - return f; -} - -/** - * Calculates the multivariate Gaussian probability density function - * and also the gradients with respect to the mean and the variance - * - * Example use: - * @code - * Vector x, mean, g_mean, g_cov; - * ArrayList d_cov; // the dSigma - * .... - * long double f = phi(x, mean, cov, d_cov, &g_mean, &g_cov); - * @endcode - */ - -long double phi(Vector& x, Vector& mean, Matrix& cov, ArrayList& d_cov, Vector *g_mean, Vector *g_cov){ - - long double det, f; - double exponent; - index_t dim; - Matrix inv; - Vector diff, tmp; - - dim = x.length(); - la::InverseInit(cov, &inv); - det = la::Determinant(cov); - - if( det < 0){ - det = -det; - } - la::SubInit(mean,x,&diff); - la::MulInit(inv, diff, &tmp); - exponent = la::Dot(diff, tmp); - long double tmp1, tmp2, tmp3; - tmp1 = 1; - tmp2 = dim; - tmp2 = tmp2/2; - tmp2 = pow((2*(math::PI)),tmp2); - tmp1 = tmp1/tmp2; - tmp3 = 1; - tmp2 = sqrt(det); - tmp3 = tmp3/tmp2; - tmp2 = -exponent; - tmp2 = tmp2 / 2; - f = (tmp1*tmp3*exp(tmp2)); - - // Calculating the g_mean values which would be a (1 X dim) vector - la::ScaleInit(f,tmp,g_mean); - - // Calculating the g_cov values which would be a (1 X (dim*(dim+1)/2)) vector - double *g_cov_tmp; - g_cov_tmp = (double*)malloc(d_cov.size()*sizeof(double)); - for(index_t i = 0; i < d_cov.size(); i++){ - Vector tmp_d; - // Matrix inv_d; - Matrix inv_d, tmp_mat_1, tmp_mat_2; - double tmp_d_cov_d_r; - - la::MulInit(d_cov[i],inv,&tmp_mat_1); - la::MulInit(inv, tmp_mat_1, &tmp_mat_2); - la::MulInit(tmp_mat_2, diff, &tmp_d); - tmp_d_cov_d_r = la::Dot(diff, tmp_d); - - // la::MulInit(d_cov[i], tmp, &tmp_d); - // tmp_d_cov_d_r = la::Dot(tmp_d,tmp); - - la::MulInit(inv,d_cov[i],&inv_d); - - double trace = 0; - for(index_t j = 0; j < dim; j++) { - trace += inv_d.get(j,j); - } - - tmp_d_cov_d_r -= trace; - //printf("trace = %lf\n", trace); - - g_cov_tmp[i] = f*tmp_d_cov_d_r/2; - } - g_cov->Copy(g_cov_tmp,d_cov.size()); - - return f; -}